Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University, College of Medicine, Seoul, Korea.
Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University, College of Medicine, Seoul, Korea.
PLoS One. 2020 Feb 13;15(2):e0228968. doi: 10.1371/journal.pone.0228968. eCollection 2020.
To evaluate whether if ultrasonography (US)-based radiomics enables prediction of the presence of BRAFV600E mutations among patients diagnosed as papillary thyroid carcninoma (PTC).
From December 2015 to May 2017, 527 patients who had been treated surgically for PTC were included (training: 387, validation: 140). All patients had BRAFV600E mutation analysis performed on surgical specimen. Feature extraction was performed using preoperative US images of the 527 patients (mean size of PTC: 16.4mm±7.9, range, 10-85 mm). A Radiomics Score was generated by using the least absolute shrinkage and selection operator (LASSO) regression model. Univariable/multivariable logistic regression analysis was performed to evaluate the factors including Radiomics Score in predicting BRAFV600E mutation. Subgroup analysis including conventional PTC <20-mm (n = 389) was performed (training: 280, validation: 109).
Of the 527 patients diagnosed with PTC, 428 (81.2%) were positive and 99 (18.8%) were negative for BRAFV600E mutation. In both total 527 cancers and 389 conventional PTC<20-mm, Radiomics Score was the single factor showing significant association to the presence of BRAFV600E mutation on multivariable analysis (all P<0.05). C-statistics for the validation set in the total cancers and the conventional PTCs<20-mm were lower than that of the training set: 0.629 (95% CI: 0.516-0.742) to 0.718 (95% CI: 0.650-0.786), and 0.567 (95% CI: 0.434-0.699) to 0.729 (95% CI: 0.632-0.826), respectively.
Radiomics features extracted from US has limited value as a non-invasive biomarker for predicting the presence of BRAFV600E mutation status of PTC regardless of size.
评估基于超声的放射组学是否能够预测诊断为甲状腺乳头状癌（PTC）患者的 BRAFV600E 突变。
从 2015 年 12 月至 2017 年 5 月，纳入了 527 例接受手术治疗的 PTC 患者（训练组：387 例，验证组：140 例）。所有患者均对手术标本进行 BRAFV600E 突变分析。使用 527 例患者的术前超声图像进行特征提取（PTC 的平均大小：16.4mm±7.9，范围 10-85mm）。采用最小绝对收缩和选择算子（LASSO）回归模型生成放射组学评分。采用单变量/多变量逻辑回归分析评估包括放射组学评分在内的因素对 BRAFV600E 突变的预测作用。对包括常规 PTC<20mm（n=389）在内的亚组进行了分析（训练组：280 例，验证组：109 例）。
在诊断为 PTC 的 527 例患者中，428 例（81.2%）为 BRAFV600E 突变阳性，99 例（18.8%）为 BRAFV600E 突变阴性。在总 527 例癌症和 389 例常规 PTC<20mm 中，放射组学评分是多变量分析中唯一与 BRAFV600E 突变存在显著相关性的因素（均 P<0.05）。在总癌症和常规 PTC<20mm 的验证组中，C 统计量低于训练组：0.629（95%CI：0.516-0.742）至 0.718（95%CI：0.650-0.786），以及 0.567（95%CI：0.434-0.699）至 0.729（95%CI：0.632-0.826）。
无论肿瘤大小如何，从超声提取的放射组学特征作为预测 PTC BRAFV600E 突变状态的非侵入性生物标志物的价值有限。
